{"id":"W3136194041","doi":"10.1109/ictmod49425.2020.9380587","title":"Data Science and Strategic Complexity","year":2020,"lang":"en","type":"article","venue":"","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Big data; Competition (biology); Phenomenon; Competitive intelligence; Blindness; Data science; Computer science; Noise (video); Risk analysis (engineering); Business; Knowledge management; Artificial intelligence; Data mining; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003459335,0.00004301861,0.0001077712,0.00004557005,0.00007373534,0.00007262259,0.0003982138,0.00002803921,0.0004080808],"category_scores_gemma":[0.0001197147,0.00004361606,0.000004674967,0.0003114511,0.0003264467,0.0003809331,0.0002954191,0.00006484367,0.0002996744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000151695,"about_ca_system_score_gemma":0.00001253155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003025693,"about_ca_topic_score_gemma":0.000004346864,"domain_scores_codex":[0.9993287,8.452776e-7,0.0001971809,0.0003560862,0.00001275056,0.0001044096],"domain_scores_gemma":[0.999617,0.000006948158,0.00006942057,0.0002398818,0.00001791114,0.00004887062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[7.459184e-7,0.000005203721,0.004663579,0.000002469457,0.000001639203,2.074704e-7,0.000006631332,2.024966e-7,0.00003929327,0.9947532,0.0002573042,0.0002694916],"study_design_scores_gemma":[0.0001626173,0.00004141634,0.01026658,7.237623e-7,5.91097e-7,0.000001057897,0.00009511432,0.05904574,0.00008003422,0.9123344,0.01784898,0.0001227391],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.604144,0.0001361145,0.002123414,0.01105458,0.00004479853,0.00007587462,0.0001404798,0.0001015367,0.3821792],"genre_scores_gemma":[0.9953498,0.00001761001,0.002859129,0.001700493,0.00002236534,8.985045e-7,0.00001234353,0.000002416525,0.00003492458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3912058,"threshold_uncertainty_score":0.4468199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5562792680358895,"score_gpt":0.2929133952895174,"score_spread":0.263365872746372,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}